5 accepted papers
Existing cross-network node classification methods are mainly proposed for closed-set setting, where the source network and the target network share exactly the same label space. Such a setting is restricted in real-world applications, since the target network might contain additional classes that a…
Neural Radiance Fields (NeRF) has recently overhauled novel view synthesis, but it requires extensive computations for training and captures variations in detail with difficulty. In this paper, we propose a novel framework, termed CD-TDRF, to mitigate these dilemmas. CD-TDRF factorizes a density vox…
The pre-training architectures of large language models encompass various types including autoencoding models autoregressive models and encoder-decoder models. We posit that any modality can potentially benefit from a large language model as long as it undergoes vector quantization to become discret…
The scarcity of annotated data has sparked significant interest in unsupervised pre-training methods that leverage medical reports as auxiliary signals for medical visual representation learning. However existing research overlooks the multi-granularity nature of medical visual representation and la…
Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph datasets. In addition, the contrastive losses or…